Robust Stabilization and Control of Takagi–Sugeno Fuzzy Systems with Parameter Uncertainties and Disturbances via State Feedback and Output Feedback

2019 ◽  
Vol 21 (8) ◽  
pp. 2556-2574 ◽  
Author(s):  
A. K. Iqbal Ahammed ◽  
Mohammed Fazle Azeem
Keyword(s):  
Output Feedback ◽  
Fuzzy Systems ◽  
State Feedback ◽  
Robust Stabilization ◽  
Parameter Uncertainties ◽  
Takagi Sugeno ◽  
And Control

Robust Stabilization And Control Of Takagi-Sugeno Fuzzy Systems With Parameter Uncertainties And Disturbances Via State Feedback And Output Feedback

2020 ◽  
Vol 9 (3) ◽  
pp. 63-99
Author(s):  
Iqbal Ahammed A.K. ◽  
Mohammed Fazle Azeem
Keyword(s):  
Output Feedback ◽  
Fuzzy Systems ◽  
State Feedback ◽  
Robust Stabilization ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Ts Fuzzy Model ◽  
Takagi Sugeno ◽  
And Control

Most of the systems in the industry contain extreme non-linearity and uncertainties, which are hard to design and control utilizing general nonlinear systems. To conquer this sort of troubles, different plans have been produced in the most recent two decades, among which a popular methodology is Takagi-Sugeno fuzzy control. In this article, we present robust stabilization and control of Takagi-Sugeno (T-S) fuzzy systems with parameter uncertainties and disturbances. Initially, Takagi and Sugeno (TS) fuzzy model is used to represent a nonlinear system. Based on this T-S fuzzy model, fuzzy controller design schemes for state feedback and output feedback is also developed. Then, necessary conditions are derived for robust stabilization in the intelligence of Lyapunov asymptotic stability and are expressed in the arrangement of linear matrix inequalities (LMIs). The proposed system is implemented in the working platform of MATLAB and the simulation results are provided to illustrate the effectiveness of the proposed methods.

Robust Output Feedback Guaranteed Cost Control of Uncertain Fuzzy Systems with Immeasurable Premise Variables

2007 ◽  
Vol 11 (7) ◽  
pp. 745-750 ◽  
Author(s):  
Jun Yoneyama ◽  
Keyword(s):  
Output Feedback ◽  
Fuzzy Systems ◽  
Robust Stabilization ◽  
Output Feedback Control ◽  
The State ◽  
Feedback Stabilization ◽  
Output Feedback Stabilization ◽  
Guaranteed Cost ◽  
Takagi Sugeno ◽  
Uncertain Linear Systems

This paper is concerned with robust output feedback stabilization with guaranteed cost of uncertain Takagi-Sugeno fuzzy systems with immeasurable premise variables. When we consider Takagi-Sugeno fuzzy systems, the selection of premise variables plays an important role. If the premise variable is the state of the system, then a fuzzy system describes a wide class of nonlinear systems. However, the state is not measurable in the output feedback control problem. In this case, a control design based on parallel distributed compensation is impossible because a controller depends on immeasurable premise variables. In this paper, we consider the robust output feedback stabilization problem with guaranteed cost where the premise variable is not measurable. We formulate this problem as a robust stabilization of uncertain linear systems. Numerical examples are given to illustrate our theory.

Robust Adaptive Control of Nonlinear Output Feedback Systems Under Disturbances With Unknown Bounds

2004 ◽  
Vol 126 (1) ◽  
pp. 229-235 ◽  
Author(s):  
Dong H. Kim ◽  
Hua O. Wang ◽  
Hai-Won Yang
Keyword(s):  
Output Feedback ◽  
State Feedback ◽  
Sliding Mode ◽  
Design Procedure ◽  
Adaptation Strategy ◽  
Robust Adaptive Control ◽  
Feedback Systems ◽  
Adaptive Controllers ◽  
Robust Adaptive ◽  
And Control

This paper describes a systematic procedure to design robust adaptive controllers for a class of nonlinear systems with unknown functions of unknown bounds based on backstepping and sliding mode techniques. These unknown functions can be unmodeled system nonlinearities, uncertainties and disturbances with unknown bounds. Both state feedback and output feedback designs are addressed. In the design procedure, the upper bounds of the unknown functions are estimated using an adaptation strategy, and the estimates are used to design stabilizing functions and control inputs based on the backstepping design methodology. The proposed controllers guarantee that the tracking errors converge to a residual set close to zero exponentially for both state feedback and output feedback designs, while maintaining the boundedness of all other variables.

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